.. _sphx_glr_auto_examples_ensemble_plot_forest_importances_faces.py: ================================================= Pixel importances with a parallel forest of trees ================================================= This example shows the use of forests of trees to evaluate the importance of the pixels in an image classification task (faces). The hotter the pixel, the more important. The code below also illustrates how the construction and the computation of the predictions can be parallelized within multiple jobs. .. image:: /auto_examples/ensemble/images/sphx_glr_plot_forest_importances_faces_001.png :align: center .. rst-class:: sphx-glr-script-out Out:: Fitting ExtraTreesClassifier on faces data with 1 cores... done in 3.359s | .. code-block:: python print(__doc__) from time import time import matplotlib.pyplot as plt from sklearn.datasets import fetch_olivetti_faces from sklearn.ensemble import ExtraTreesClassifier # Number of cores to use to perform parallel fitting of the forest model n_jobs = 1 # Load the faces dataset data = fetch_olivetti_faces() X = data.images.reshape((len(data.images), -1)) y = data.target mask = y < 5 # Limit to 5 classes X = X[mask] y = y[mask] # Build a forest and compute the pixel importances print("Fitting ExtraTreesClassifier on faces data with %d cores..." % n_jobs) t0 = time() forest = ExtraTreesClassifier(n_estimators=1000, max_features=128, n_jobs=n_jobs, random_state=0) forest.fit(X, y) print("done in %0.3fs" % (time() - t0)) importances = forest.feature_importances_ importances = importances.reshape(data.images[0].shape) # Plot pixel importances plt.matshow(importances, cmap=plt.cm.hot) plt.title("Pixel importances with forests of trees") plt.show() **Total running time of the script:** (0 minutes 4.096 seconds) .. container:: sphx-glr-download **Download Python source code:** :download:`plot_forest_importances_faces.py ` .. container:: sphx-glr-download **Download IPython notebook:** :download:`plot_forest_importances_faces.ipynb `